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README.md

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+---
+title: ChatModelApp
+emoji: ⚡
+colorFrom: gray
+colorTo: red
+sdk: streamlit
+sdk_version: 1.30.0
+app_file: app.py
+pinned: false
+---
+
+# ChatModelApp
+
+Multi-provider chat app using **Google Gemini (PaLM API)** and **Hugging Face** models, built with Streamlit.
+
+## Usage
+
+1. Set Secrets in Hugging Face Spaces:
+   - `GEN_API_KEY` → Gemini API key  
+   - `HUGGINGFACE_HUB_TOKEN` → Hugging Face token
+2. The app file is `app.py`. It will run automatically.

app.py

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+import streamlit as st
+from huggingface_hub import InferenceClient
+import google.generativeai as genai
+import time
+import json # Import json for better handling of HF client response
+
+# -------------------
+# API Keys Setup
+# -------------------
+# Use Streamlit's built-in secrets handling
+huggingface_token = st.secrets.get("HUGGINGFACE_HUB_TOKEN", "")
+gemini_api_key = st.secrets.get("GEN_API_KEY", "")
+
+# -------------------
+# Configuration
+# -------------------
+st.set_page_config(page_title="Multi-Provider Chat", layout="wide")
+st.title("⚡ Multi-Provider Chat App")
+
+# List of recommended Hugging Face models that work well for chat via InferenceClient
+# All instruction-tuned models (Mistral, Zephyr, Gemma) are failing due to server 
+# restrictions requiring the 'conversational' task or brittle templating. 
+# Switching to small, reliable base models guaranteed to support 'text-generation'.
+HF_RECOMMENDED_MODELS = [
+    "gpt2",                       # New primary fallback: Very stable base model
+    "bigscience/bloom-560m",      # Kept as secondary base model
+]
+
+# -------------------
+# Sidebar Settings
+# -------------------
+st.sidebar.title("⚙️ Settings")
+provider = st.sidebar.selectbox("Provider", ["Hugging Face", "Gemini"])
+
+# -------------------
+# Provider Setup
+# -------------------
+client = None
+model = None
+
+if provider == "Hugging Face":
+    if not huggingface_token:
+        st.error("⚠️ Please set your 'HUGGINGFACE_HUB_TOKEN' in Streamlit secrets.")
+        st.stop()
+    
+    # Initialize the client
+    client = InferenceClient(token=huggingface_token)
+
+    selected_models = st.sidebar.multiselect(
+        "Choose HF models", 
+        HF_RECOMMENDED_MODELS, 
+        default=[HF_RECOMMENDED_MODELS[0]]
+    )
+    if not selected_models:
+        st.warning("⚠️ Please select at least one Hugging Face model.")
+        st.stop()
+
+elif provider == "Gemini":
+    if not gemini_api_key:
+        st.error("⚠️ Please set your 'GEN_API_KEY' in Streamlit secrets.")
+        st.stop()
+        
+    genai.configure(api_key=gemini_api_key)
+    
+    # Fetch available models that support the generateContent method
+    available_models = [
+        m.name for m in genai.list_models() if "generateContent" in m.supported_generation_methods
+    ]
+    
+    if not available_models:
+        st.error("⚠️ No Gemini models available for your API key.")
+        st.stop()
+        
+    model = st.sidebar.selectbox("Model", available_models)
+
+    # Initialize Gemini chat if model changes or if not initialized
+    if "gemini_chat" not in st.session_state or st.session_state.get("model") != model:
+        st.session_state.model = model
+        try:
+            gemini_model = genai.GenerativeModel(model)
+            st.session_state.gemini_chat = gemini_model.start_chat(history=[])
+        except Exception as e:
+            st.error(f"⚠️ Could not initialize Gemini model: {e}")
+            st.stop()
+
+# -------------------
+# System Prompt
+# -------------------
+system_prompt = st.sidebar.text_area(
+    "System Prompt", 
+    "You are a helpful AI assistant. Provide concise and accurate answers."
+)
+
+# -------------------
+# Chat History State
+# -------------------
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Reset conversation button
+if st.sidebar.button("Reset Conversation"):
+    st.session_state.messages = []
+    # Also reset the Gemini chat history if using Gemini
+    if provider == "Gemini" and model:
+        gemini_model = genai.GenerativeModel(model)
+        st.session_state.gemini_chat = gemini_model.start_chat(history=[])
+    st.rerun() # Rerun to clear messages immediately
+
+# -------------------
+# Display Chat Messages
+# -------------------
+for msg in st.session_state.messages:
+    with st.chat_message(msg["role"]):
+        st.markdown(msg["content"])
+
+# -------------------
+# User Input
+# -------------------
+if user_input := st.chat_input("Type your message..."):
+    # 1. Display and save user message immediately
+    st.chat_message("user").markdown(user_input)
+    st.session_state.messages.append({"role": "user", "content": user_input})
+    
+    # -------------------
+    # Hugging Face Logic
+    # -------------------
+    if provider == "Hugging Face":
+        for m in selected_models:
+            # Display a temporary "generating" message 
+            with st.chat_message("assistant"):
+                message_placeholder = st.empty()
+                message_placeholder.markdown(f"**{m}** is generating...")
+
+            try:
+                bot_text = ""
+                # Use simple stop sequences for chat formatting, including "assistant:" itself
+                stop_sequences = ["assistant:", "user:"] 
+                prompt_text = ""
+                
+                # --- Generic Chat Template (Most reliable for text-generation endpoint) ---
+                # This uses the simple "role: content" format which is often robust.
+                conv = "\n".join([f"{msg['role']}: {msg['content']}" for msg in st.session_state.messages])
+                prompt_text = f"{system_prompt}\n\n{conv}\nassistant:"
+                
+                # 2. Generate response using text_generation 
+                resp = client.text_generation(
+                    model=m,
+                    prompt=prompt_text,
+                    max_new_tokens=256,
+                    temperature=0.7,
+                    stop_sequences=stop_sequences
+                )
+
+                # 3. Unified parsing
+                if isinstance(resp, str):
+                    bot_text = resp
+                elif isinstance(resp, dict) and "generated_text" in resp:
+                    bot_text = resp["generated_text"]
+                elif isinstance(resp, list) and resp and "generated_text" in resp[0]:
+                    bot_text = resp[0]["generated_text"]
+                
+                # Clean up prompt from response if model echoes it (common behavior for text_generation)
+                if bot_text.startswith(prompt_text):
+                    bot_text = bot_text[len(prompt_text):].strip()
+                
+            except Exception as e:
+                # Catching connection errors or specific API deployment issues
+                bot_text = f"⚠️ Error with **{m}**: Model could not generate a response. ({type(e).__name__}: {e})"
+            
+            # 4. Display and save final response (common logic for all models)
+            final_response = f"**{m}**\n\n{bot_text}"
+            
+            # Update the temporary placeholder with the final response
+            message_placeholder.markdown(final_response)
+            
+            # Save the final response to chat history
+            st.session_state.messages.append({"role": "assistant", "content": final_response})
+            st.rerun() # Rerun to update the display properly after generation
+
+    # -------------------
+    # Gemini Logic
+    # -------------------
+    elif provider == "Gemini":
+        try:
+            if user_input.strip():
+                with st.spinner("Gemini is thinking..."):
+                    resp = st.session_state.gemini_chat.send_message(user_input)
+                
+                bot_text = resp.text
+            else:
+                bot_text = "⚠️ Please enter a message before sending."
+
+        except Exception as e:
+            bot_text = f"⚠️ Gemini could not respond right now. Please try again. ({e})"
+        
+        # Display and save assistant response
+        with st.chat_message("assistant"):
+            st.markdown(bot_text)
+        
+        st.session_state.messages.append({"role": "assistant", "content": bot_text})
+        st.rerun()

requirements (1).txt

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+streamlit==1.30.0
+streamlit-chat
+huggingface-hub>=0.21,<1.0.0
+google-generativeai==0.3.0
+datasets
+protobuf<4
+pydantic>=2.0,<3.0
+
+