Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+from scrapegraphai.graphs import SmartScraperGraph
+import torch
+
+# Page config
+st.set_page_config(
+    page_title="Zephyr Chat & Scrape",
+    page_icon="🤖",
+    layout="wide"
+)
+
+# Initialize session state
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+if "scrape_results" not in st.session_state:
+    st.session_state.scrape_results = None
+
+# Load Zephyr model
+@st.cache_resource
+def load_model():
+    return pipeline(
+        "text-generation",
+        model="HuggingFaceH4/zephyr-7b-beta",
+        torch_dtype=torch.float16,
+        device_map="auto",
+    )
+
+# Initialize the model
+model = load_model()
+
+# Sidebar for web scraping
+with st.sidebar:
+    st.title("Web Scraping")
+    url = st.text_input("Enter URL to scrape")
+    scrape_prompt = st.text_input("What information do you want to extract?")
+    
+    if st.button("Scrape"):
+        try:
+            # Configure scraper
+            graph_config = {
+                "llm": {
+                    "model": "HuggingFaceH4/zephyr-7b-beta",
+                    "temperature": 0.7,
+                },
+                "verbose": True
+            }
+            
+            # Create scraper instance
+            scraper = SmartScraperGraph(
+                prompt=scrape_prompt,
+                source=url,
+                config=graph_config
+            )
+            
+            # Run scraping
+            st.session_state.scrape_results = scraper.run()
+            st.success("Scraping completed!")
+            
+        except Exception as e:
+            st.error(f"Error during scraping: {str(e)}")
+
+# Main chat interface
+st.title("Zephyr Chatbot 🤖")
+
+# Display scraped results if available
+if st.session_state.scrape_results:
+    st.subheader("Scraped Information")
+    st.json(st.session_state.scrape_results)
+    
+# Display chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Chat input
+if prompt := st.chat_input("What's on your mind?"):
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+    
+    # Generate response
+    with st.chat_message("assistant"):
+        with st.spinner("Thinking..."):
+            # Include scraped content in context if available
+            context = ""
+            if st.session_state.scrape_results:
+                context = f"Scraped information: {str(st.session_state.scrape_results)}\n"
+            
+            full_prompt = f"{context}User: {prompt}\nAssistant:"
+            
+            response = model(
+                full_prompt,
+                max_length=1000,
+                temperature=0.7,
+                top_p=0.95,
+                repetition_penalty=1.15
+            )[0]["generated_text"]
+            
+            # Clean up response to get only the assistant's reply
+            response = response.split("Assistant:")[-1].strip()
+            
+            st.markdown(response)
+            st.session_state.messages.append({"role": "assistant", "content": response})