Trying to sanitize the file names

gemini/activate.sh

@@ -0,0 +1 @@
+source gemini_env/bin/activate

gemini/gemini.py

@@ -3,19 +3,88 @@ import argparse
 import json
 import os
 import sys
+import urllib.request
 from google import genai
 from google.genai import types
 
+PRICING_FILE = ".gemini_pricing.json"
+
+def fetch_pricing_for_model(client, target_model):
+    url = "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing"
+    print(f"Fetching live pricing for {target_model} from the web...")
+    
+    try:
+        req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
+        with urllib.request.urlopen(req) as response:
+            html = response.read().decode('utf-8')
+    except Exception as e:
+        print(f"Warning: Failed to fetch HTML from {url}: {e}", file=sys.stderr)
+        return None
+
+    prompt = f"""
+    Extract the API pricing for the model '{target_model}' from the following HTML text.
+    Find the cost per 1 million tokens for input, cached content, and output.
+    Many models have a split tier where the cost increases if the prompt exceeds 200k tokens.
+    
+    CRITICAL: If the model '{target_model}' is definitively NOT found in the HTML data, return an empty JSON object: {{}}
+    
+    Otherwise, return ONLY a valid JSON object with this exact structure:
+    {{
+        "{target_model}": {{
+            "input": 0.00,
+            "cached": 0.00,
+            "output": 0.00,
+            "input_over_200k": 0.00,
+            "cached_over_200k": 0.00,
+            "output_over_200k": 0.00
+        }}
+    }}
+    If a tier value is not found, duplicate the base tier values.
+    
+    HTML DATA:
+    {html}
+    """
+    
+    config = types.GenerateContentConfig(
+        response_mime_type="application/json",
+        temperature=0.0
+    )
+    
+    print("Parsing pricing data via background AI session...")
+    try:
+        # We use a fast, cheap model just for the parsing task
+        res = client.models.generate_content(
+            model="gemini-3.1-flash-lite", 
+            contents=prompt,
+            config=config
+        )
+        new_data = json.loads(res.text)
+        
+        # Strip the array wrapper if the AI returned a list instead of a pure dict
+        if isinstance(new_data, list) and len(new_data) > 0:
+            new_data = new_data[0]
+            
+        print(f"Successfully retrieved pricing for {target_model}.")
+        print(new_data)
+        return new_data
+    except Exception as e:
+        print(f"Warning: Failed to extract pricing using AI: {e}", file=sys.stderr)
+        return None
+
 def main():
     parser = argparse.ArgumentParser(description="Gemini API CLI with File & Context Caching")
     parser.add_argument("-c", "--context", type=str, default=None, 
-                        help="Path to context file. If omitted, files/caches are deleted after execution.")
+                        help="Path to context file. If omitted, transient mode is used.")
     parser.add_argument("-f", "--files", nargs="+", default=[], 
                         help="Files to upload to the Gemini API")
     parser.add_argument("-m", "--model", type=str, default="gemini-3.1-flash-lite",
                         help="The model to use (default: gemini-3.1-flash-lite)")
     parser.add_argument("-d", "--destroy", action="store_true",
-                        help="Destroy the cloud files and cache, and delete the local context file")
+                        help="Destroy cloud files/cache, and delete local context")
+    parser.add_argument("-x", "--clear-history", action="store_true",
+                        help="Clear the conversation history without destroying files/caches")
+    parser.add_argument("--pricing", action="store_true",
+                        help="Force update the pricing info for the specified model from the web")
     parser.add_argument("-o", "--output", type=str,
                         help="Direct the raw output to a specific file instead of stdout")
     parser.add_argument("-p", "--prompt", type=str, 
@@ -24,7 +93,6 @@ def main():
                         help="Positional arguments treated as the prompt if -p is omitted")
 
     args = parser.parse_args()
-
     prompt_text = args.prompt if args.prompt else " ".join(args.positional_prompt) if args.positional_prompt else None
 
     if not os.environ.get("GEMINI_API_KEY"):
@@ -32,26 +100,74 @@ def main():
         sys.exit(1)
         
     client = genai.Client()
-    context_data = {"file_ids": [], "cache_id": None}
+
+    # ---------------------------------------------------------
+    # PRICING CONFIGURATION
+    # ---------------------------------------------------------
+    pricing_data = {}
+    
+    if os.path.exists(PRICING_FILE):
+        try:
+            with open(PRICING_FILE, "r") as f:
+                pricing_data = json.load(f)
+        except json.JSONDecodeError:
+            print(f"Warning: {PRICING_FILE} is corrupted. Starting fresh.", file=sys.stderr)
+
+    # Fetch pricing if forced, or if the model isn't currently tracked
+    if args.pricing or args.model not in pricing_data:
+        new_pricing = fetch_pricing_for_model(client, args.model)
+        
+        if new_pricing and args.model in new_pricing:
+            pricing_data.update(new_pricing)
+            with open(PRICING_FILE, "w") as f:
+                json.dump(pricing_data, f, indent=4)
+        else:
+            print(f"Warning: Could not fetch pricing for {args.model}. Estimating cost at $0.00.", file=sys.stderr)
+            # Add a fallback zero-value so the script doesn't crash during cost calculation
+            pricing_data[args.model] = {"input": 0.0, "cached": 0.0, "output": 0.0}
+            
+        # If the user only requested a pricing update and nothing else, exit cleanly
+        if args.pricing and not prompt_text and not args.files and not args.destroy and not args.clear_history:
+            return
+            
+    # ---------------------------------------------------------
+    # STATE MANAGEMENT
+    # ---------------------------------------------------------
+    context_data = {"file_ids": [], "caches": {}, "history": []}
 
     if args.context and os.path.exists(args.context):
         try:
             with open(args.context, "r") as f:
-                context_data = json.load(f)
+                loaded_data = json.load(f)
+                context_data["file_ids"] = loaded_data.get("file_ids", [])
+                context_data["caches"] = loaded_data.get("caches", {})
+                context_data["history"] = loaded_data.get("history", [])
         except json.JSONDecodeError:
             print(f"Warning: Could not parse {args.context}. Starting fresh.", file=sys.stderr)
 
+    # ---------------------------------------------------------
+    # CLEAR HISTORY FLAG
+    # ---------------------------------------------------------
+    if args.clear_history:
+        context_data["history"] = []
+        if args.context:
+            with open(args.context, "w") as f:
+                json.dump(context_data, f, indent=4)
+        print("Conversation history cleared.")
+        if not prompt_text and not args.files and not args.destroy:
+            return
+
     # ---------------------------------------------------------
     # DESTROY FLAG LOGIC
     # ---------------------------------------------------------
     if args.destroy:
         print("Destroying server resources and local context...")
-        if context_data.get("cache_id"):
+        for model_name, cache_info in context_data.get("caches", {}).items():
             try:
-                client.caches.delete(name=context_data["cache_id"])
+                client.caches.delete(name=cache_info["cache_id"])
-                print(f"Deleted cache: {context_data['cache_id']}")
+                print(f"Deleted cache for {model_name}: {cache_info['cache_id']}")
             except Exception as e:
-                print(f"Warning: Failed to delete cache. {e}", file=sys.stderr)
+                print(f"Warning: Failed to delete cache for {model_name}. {e}", file=sys.stderr)
         
         for file_id in context_data.get("file_ids", []):
             try:
@@ -71,27 +187,34 @@ def main():
         # ---------------------------------------------------------
         # UPLOAD LOGIC
         # ---------------------------------------------------------
-        new_files_added = False
         if args.files:
             for file_path in args.files:
                 if not os.path.exists(file_path):
                     print(f"Warning: File '{file_path}' not found. Skipping.", file=sys.stderr)
                     continue
                 
-                print(f"Uploading '{file_path}'...")
+                # Sanitize the filename for HTTP headers (replace non-ASCII with underscores)
-                uploaded_file = client.files.upload(file=file_path)
+                base_name = os.path.basename(file_path)
-                print(f"Success: '{file_path}' uploaded as '{uploaded_file.name}'")
+                safe_name = "".join([c if ord(c) < 128 else "_" for c in base_name])
+                
+                print(f"Uploading '{file_path}'...", file=sys.stderr)
+                
+                # Force the SDK to use our sanitized name for the upload display name
+                uploaded_file = client.files.upload(
+                    file=file_path,
+                    config={'display_name': safe_name}
+                )
+                print(f"Success: '{file_path}' uploaded as '{uploaded_file.name}'", file=sys.stderr)
                 
                 if uploaded_file.name not in context_data["file_ids"]:
                     context_data["file_ids"].append(uploaded_file.name)
-                    new_files_added = True
 
             if args.context:
                 with open(args.context, "w") as f:
                     json.dump(context_data, f, indent=4)
 
         # ---------------------------------------------------------
-        # CACHE CREATION LOGIC
+        # CACHE VALIDATION & CREATION LOGIC
         # ---------------------------------------------------------
         system_instruction = (
             "You are a hybrid data extraction tool. If a specific format or file format output is requested "
@@ -100,23 +223,47 @@ def main():
             "and output raw data only. If not specific data format is suggested, you can answer with conversational text."
         )
 
-        # If new files were added but a cache already exists, we must destroy the stale cache.
-        if new_files_added and context_data.get("cache_id"):
-            print("New files detected. Destroying stale cache to rebuild...")
-            try:
-                client.caches.delete(name=context_data["cache_id"])
-            except Exception as e:
-                print(f"Warning: Could not delete stale cache. {e}", file=sys.stderr)
-            context_data["cache_id"] = None
-
         cache_too_small = False
         file_objects = []
+        active_cache_id = None
 
         if context_data.get("file_ids"):
             file_objects = [client.files.get(name=f_id) for f_id in context_data["file_ids"]]
+            
+            model_cache_info = context_data["caches"].get(args.model)
+            current_files_set = set(context_data["file_ids"])
+            rebuild_cache = False
+            
+            if model_cache_info:
+                cached_files_set = set(model_cache_info.get("cached_file_ids", []))
+                
+                # Check 1: Have the files changed?
+                if current_files_set != cached_files_set:
+                    print(f"File list changed. Destroying stale {args.model} cache to rebuild...")
+                    try:
+                        client.caches.delete(name=model_cache_info["cache_id"])
+                    except Exception as e:
+                        print(f"Warning: Could not delete stale cache. {e}", file=sys.stderr)
+                    rebuild_cache = True
+                else:
+                    # Check 2: Does the cache still exist on the server?
+                    print(f"Verifying existing cache for {args.model}: {model_cache_info['cache_id']}...")
+                    try:
+                        client.caches.get(name=model_cache_info['cache_id'])
+                        print("Cache verified. Extending TTL by 60 minutes...")
+                        client.caches.update(
+                            name=model_cache_info['cache_id'],
+                            config=types.UpdateCachedContentConfig(ttl="3600s")
+                        )
+                        active_cache_id = model_cache_info['cache_id']
+                    except Exception as e:
+                        print(f"Cache expired or not found. Flagging for rebuild. ({e})")
+                        rebuild_cache = True
+            else:
+                rebuild_cache = True
 
-            if not context_data.get("cache_id"):
+            if rebuild_cache:
-                print("Attempting to create Context Cache on Google's servers...")
+                print(f"Attempting to create Context Cache for {args.model}...")
                 try:
                     cache = client.caches.create(
                         model=args.model,
@@ -126,7 +273,13 @@ def main():
                             ttl="3600s" 
                         )
                     )
-                    context_data["cache_id"] = cache.name
+                    
+                    context_data["caches"][args.model] = {
+                        "cache_id": cache.name,
+                        "cached_file_ids": list(context_data["file_ids"])
+                    }
+                    active_cache_id = cache.name
+                    
                     if args.context:
                         with open(args.context, "w") as f:
                             json.dump(context_data, f, indent=4)
@@ -138,20 +291,9 @@ def main():
                         cache_too_small = True
                     else:
                         raise e
-                
-            elif not cache_too_small:
-                print(f"Loading existing cache: {context_data['cache_id']}")
-                print("Extending cache TTL by 60 minutes...")
-                try:
-                    client.caches.update(
-                        name=context_data["cache_id"],
-                        config=types.UpdateCachedContentConfig(ttl="3600s")
-                    )
-                except Exception as e:
-                     print(f"Warning: Failed to update cache TTL. {e}")
 
         # ---------------------------------------------------------
-        # GENERATION LOGIC
+        # GENERATION LOGIC (WITH HISTORY)
         # ---------------------------------------------------------
         if prompt_text:
             config_kwargs = {
@@ -159,38 +301,132 @@ def main():
                 "temperature": 0.0
             }
 
-            generation_contents = []
+            if active_cache_id and not cache_too_small:
-            
+                config_kwargs["cached_content"] = active_cache_id
-            if context_data.get("cache_id") and not cache_too_small:
-                config_kwargs["cached_content"] = context_data["cache_id"]
             else:
-                generation_contents.extend(file_objects)
                 config_kwargs["system_instruction"] = system_instruction
+
+            # Build the chat payload using strict dictionary representations
+            api_contents = []
+            files_added_to_payload = False
+
+            for msg in context_data.get("history", []):
+                parts = []
+                # If we aren't using a cache, attach un-cached files to the very first user message
+                if msg["role"] == "user" and not files_added_to_payload and not active_cache_id and file_objects:
+                    for f in file_objects:
+                        parts.append({"file_data": {"file_uri": f.uri, "mime_type": f.mime_type}})
+                    files_added_to_payload = True
+                    
+                parts.append({"text": msg["text"]})
+                api_contents.append({"role": msg["role"], "parts": parts})
+
+            current_parts = []
+            if not files_added_to_payload and not active_cache_id and file_objects:
+                for f in file_objects:
+                    current_parts.append({"file_data": {"file_uri": f.uri, "mime_type": f.mime_type}})
+                files_added_to_payload = True
                 
-            generation_contents.append(prompt_text)
+            current_parts.append({"text": prompt_text})
+            api_contents.append({"role": "user", "parts": current_parts})
+
             config = types.GenerateContentConfig(**config_kwargs)
 
             print("Generating response (this may take a moment for large outputs)...")
             
-            response_stream = client.models.generate_content_stream(
+            full_response_text = ""
-                model=args.model,
+            usage_metadata = None
-                contents=generation_contents,
+            finish_reason_str = "UNKNOWN"
-                config=config
+
-            )
+            try:
-            
+                response_stream = client.models.generate_content_stream(
-            if args.output:
+                    model=args.model,
-                with open(args.output, "w") as f:
+                    contents=api_contents,
+                    config=config
+                )
+                
+                if args.output:
+                    with open(args.output, "w") as f:
+                        for chunk in response_stream:
+                            if chunk.text:
+                                f.write(chunk.text)
+                                f.flush()
+                                full_response_text += chunk.text
+                            if chunk.usage_metadata:
+                                usage_metadata = chunk.usage_metadata
+                            if chunk.candidates and chunk.candidates[0].finish_reason:
+                                finish_reason_str = chunk.candidates[0].finish_reason.name
+                    print(f"Done! Raw output saved directly to {args.output}")
+                else:
+                    print()
                     for chunk in response_stream:
                         if chunk.text:
-                            f.write(chunk.text)
+                            print(chunk.text, end="", flush=True)
-                            f.flush()
+                            full_response_text += chunk.text
-                print(f"\nDone! Raw output saved directly to {args.output}")
+                        if chunk.usage_metadata:
-            else:
+                            usage_metadata = chunk.usage_metadata
-                print("-" * 40)
+                        if chunk.candidates and chunk.candidates[0].finish_reason:
-                for chunk in response_stream:
+                            finish_reason_str = chunk.candidates[0].finish_reason.name
-                    if chunk.text:
+                    print("\n") 
-                        print(chunk.text, end="", flush=True)
+                    
-                print("\n" + "-" * 40)
+            except Exception as e:
+                # Catch 404 Model Not Found or other critical generation errors gracefully
+                if "404" in str(e) and "NOT_FOUND" in str(e):
+                    print(f"\n[Error] The model '{args.model}' does not exist or is not available.", file=sys.stderr)
+                else:
+                    print(f"\n[API Error] {e}", file=sys.stderr)
+                
+                # Exit cleanly so we don't calculate costs or save bad history
+                return
+
+            # ---------------------------------------------------------
+            # USAGE AND COST CALCULATION
+            # ---------------------------------------------------------
+            if usage_metadata:
+                prompt_tokens = usage_metadata.prompt_token_count or 0
+                output_tokens = usage_metadata.candidates_token_count or 0
+                cached_tokens = getattr(usage_metadata, 'cached_content_token_count', 0) or 0
+                
+                # Revert to max(0) to handle Google's padding discrepancy where prompt_tokens < cached_tokens
+                uncached_tokens = max(0, prompt_tokens - cached_tokens)
+                
+                # Ensure the tier logic checks the absolute largest representation of the payload
+                total_input_tokens = max(prompt_tokens, cached_tokens)
+                
+                # Fetch the rate dynamically from the parsed JSON or default to 0.0 if fetch failed
+                rates = pricing_data.get(args.model, {"input": 0.0, "cached": 0.0, "output": 0.0})
+                
+                input_rate = rates.get("input", 0.0)
+                cached_rate = rates.get("cached", 0.0)
+                output_rate = rates.get("output", 0.0)
+                tier_label = "Base Tier"
+                
+                # Apply 200k+ tier pricing if total prompt size exceeds 200k
+                if total_input_tokens > 200_000 and "input_over_200k" in rates:
+                    if rates.get("input_over_200k", 0.0) > 0:
+                        input_rate = rates["input_over_200k"]
+                        tier_label = ">200k Tier"
+                    if rates.get("cached_over_200k", 0.0) > 0:
+                        cached_rate = rates["cached_over_200k"]
+                    if rates.get("output_over_200k", 0.0) > 0:
+                        output_rate = rates["output_over_200k"]
+                
+                input_cost = (uncached_tokens / 1_000_000) * input_rate
+                cached_cost = (cached_tokens / 1_000_000) * cached_rate
+                output_cost = (output_tokens / 1_000_000) * output_rate
+                total_cost = input_cost + cached_cost + output_cost
+
+                print("\n[--- Execution Summary ---]")
+                print(f"Finish Reason: {finish_reason_str}")
+                print(f"Token Usage:   Input: {uncached_tokens:,} | Cached: {cached_tokens:,} | Output: {output_tokens:,} ({tier_label})")
+                print(f"Est. Cost:     ${total_cost:.6f} (Model: {args.model})")
+
+            context_data["history"].append({"role": "user", "text": prompt_text})
+            context_data["history"].append({"role": "model", "text": full_response_text})
+            
+            if args.context:
+                with open(args.context, "w") as f:
+                    json.dump(context_data, f, indent=4)
 
     finally:
         # ---------------------------------------------------------
@@ -198,10 +434,10 @@ def main():
         # ---------------------------------------------------------
         if not args.context and not args.destroy:
             print("\n[Transient Mode] Cleaning up resources...")
-            if context_data.get("cache_id"):
+            for model_name, cache_info in context_data.get("caches", {}).items():
                 try:
-                    client.caches.delete(name=context_data["cache_id"])
+                    client.caches.delete(name=cache_info["cache_id"])
-                    print(f"Deleted cache: {context_data['cache_id']}")
+                    print(f"Deleted cache for {model_name}: {cache_info['cache_id']}")
                 except Exception as e:
                     print(f"Warning: Failed to delete cache. {e}", file=sys.stderr)