Create app.py

app.py

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+import gradio as gr
+import requests
+import json
+import os
+
+# --- Gemini API Configuration ---
+# The API key will be automatically provided by the Canvas environment at runtime
+# if left as an empty string. DO NOT hardcode your API key here.
+API_KEY = "" # Leave as empty string for Canvas environment
+API_URL = "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent"
+
+# --- Function to call the Gemini API ---
+async def call_gemini_api(prompt: str) -> str:
+    """
+    Calls the Gemini API with the given prompt and returns the generated text.
+    """
+    headers = {
+        'Content-Type': 'application/json',
+    }
+    payload = {
+        "contents": [{"role": "user", "parts": [{"text": prompt}]}],
+    }
+
+    # Append API key to URL if available, otherwise it's handled by Canvas runtime
+    full_api_url = f"{API_URL}?key={API_KEY}" if API_KEY else API_URL
+
+    try:
+        # Use requests.post for synchronous call, or aiohttp for async if needed
+        # For Gradio, direct requests.post within the function is usually fine.
+        response = requests.post(full_api_url, headers=headers, data=json.dumps(payload))
+        response.raise_for_status() # Raise an exception for HTTP errors
+        
+        result = response.json()
+
+        if result.get("candidates") and len(result["candidates"]) > 0 and \
+           result["candidates"][0].get("content") and \
+           result["candidates"][0]["content"].get("parts") and \
+           len(result["candidates"][0]["content"]["parts"]) > 0:
+            return result["candidates"][0]["content"]["parts"][0]["text"]
+        else:
+            return "No content generated by the model."
+    except requests.exceptions.RequestException as e:
+        return f"API Call Error: {e}"
+    except json.JSONDecodeError:
+        return f"API Response Error: Could not decode JSON. Response: {response.text}"
+    except Exception as e:
+        return f"An unexpected error occurred: {e}"
+
+# --- Gradio Interface Function ---
+async def analyze_chat_conversation(chat_text: str, analysis_task: str) -> str:
+    """
+    Analyzes the chat conversation based on the selected task using an LLM.
+    """
+    if not chat_text.strip():
+        return "Please enter a chat conversation to analyze."
+
+    prompt = ""
+    if analysis_task == "Summarize":
+        prompt = f"Summarize the following chat conversation:\n\n{chat_text}\n\nSummary:"
+    elif analysis_task == "Sentiment Analysis":
+        prompt = f"Analyze the overall sentiment of the following chat conversation (e.g., positive, negative, neutral, mixed). Explain your reasoning briefly:\n\n{chat_text}\n\nSentiment Analysis:"
+    elif analysis_task == "Extract Key Points & Action Items":
+        prompt = f"Extract the main discussion points and any explicit action items from the following chat conversation. Present them as a bulleted list:\n\n{chat_text}\n\nKey Points and Action Items:"
+    else:
+        return "Invalid analysis task selected."
+
+    # Call the Gemini API
+    response_text = await call_gemini_api(prompt)
+    return response_text
+
+# --- Gradio Interface Definition ---
+demo = gr.Interface(
+    fn=analyze_chat_conversation,
+    inputs=[
+        gr.Textbox(lines=10, label="Paste Chat Conversation Here", placeholder="e.g., 'Alice: Let's meet tomorrow. Bob: Sure, 10 AM? Alice: Yes, and please bring the report. Bob: Will do.'"),
+        gr.Dropdown(
+            ["Summarize", "Sentiment Analysis", "Extract Key Points & Action Items"],
+            label="Select Analysis Task",
+            value="Summarize"
+        )
+    ],
+    outputs=gr.Textbox(label="Analysis Result", lines=15),
+    title="💬 Chat Conversation Analyzer (Powered by Gemini)",
+    description="Paste your chat conversation, select an analysis task, and get insights from an AI."
+)
+
+# --- Launch the Gradio App ---
+if __name__ == "__main__":
+    # For local testing, use demo.launch()
+    # For Hugging Face Spaces, ensure `gradio` and `requests` are in requirements.txt
+    demo.launch()